We present a new framework to reconstruct holistic 3D indoor scenes including
both room background and indoor objects from single-view images. Existing
methods can only produce 3D shapes of indoor objects with limited geometry
quality because of the heavy occlusion of indoor scenes. To solve this, we
propose an instance-aligned implicit function (InstPIFu) for detailed object
reconstruction. Combining with instance-aligned attention module, our method is
empowered to decouple mixed local features toward the occluded instances.
Additionally, unlike previous methods that simply represents the room
background as a 3D bounding box, depth map or a set of planes, we recover the
fine geometry of the background via implicit representation. Extensive
experiments on the SUN RGB-D, Pix3D, 3D-FUTURE, and 3D-FRONT datasets
demonstrate that our method outperforms existing approaches in both background
and foreground object reconstruction. Our code and model will be made publicly
available.

我们提出了一种新的框架来重建立体的室内场景，包括从单视图图像中提取的背景和室内物体。我们的方法是将实例对齐的隐式函数（InstPIFu）与对齐注意力模块相结合，从而使我们的方法能够解耦合并的局部特征以实现物体的详细重构和恢复背景的细致几何结构，实验证明了我们方法在前景和背景物体重构方面的优越性。